{
 "metadata": {
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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 },
 "nbformat": 4,
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 "cells": [
  {
   "source": [
    "## 原始数据的LogisticRegression"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "            Time         V1         V2        V3        V4        V5  \\\n0            0.0  -1.359807  -0.072781  2.536347  1.378155 -0.338321   \n1            0.0   1.191857   0.266151  0.166480  0.448154  0.060018   \n2            1.0  -1.358354  -1.340163  1.773209  0.379780 -0.503198   \n3            1.0  -0.966272  -0.185226  1.792993 -0.863291 -0.010309   \n4            2.0  -1.158233   0.877737  1.548718  0.403034 -0.407193   \n...          ...        ...        ...       ...       ...       ...   \n284802  172786.0 -11.881118  10.071785 -9.834783 -2.066656 -5.364473   \n284803  172787.0  -0.732789  -0.055080  2.035030 -0.738589  0.868229   \n284804  172788.0   1.919565  -0.301254 -3.249640 -0.557828  2.630515   \n284805  172788.0  -0.240440   0.530483  0.702510  0.689799 -0.377961   \n284806  172792.0  -0.533413  -0.189733  0.703337 -0.506271 -0.012546   \n\n              V6        V7        V8        V9  ...       V21       V22  \\\n0       0.462388  0.239599  0.098698  0.363787  ... -0.018307  0.277838   \n1      -0.082361 -0.078803  0.085102 -0.255425  ... -0.225775 -0.638672   \n2       1.800499  0.791461  0.247676 -1.514654  ...  0.247998  0.771679   \n3       1.247203  0.237609  0.377436 -1.387024  ... -0.108300  0.005274   \n4       0.095921  0.592941 -0.270533  0.817739  ... -0.009431  0.798278   \n...          ...       ...       ...       ...  ...       ...       ...   \n284802 -2.606837 -4.918215  7.305334  1.914428  ...  0.213454  0.111864   \n284803  1.058415  0.024330  0.294869  0.584800  ...  0.214205  0.924384   \n284804  3.031260 -0.296827  0.708417  0.432454  ...  0.232045  0.578229   \n284805  0.623708 -0.686180  0.679145  0.392087  ...  0.265245  0.800049   \n284806 -0.649617  1.577006 -0.414650  0.486180  ...  0.261057  0.643078   \n\n             V23       V24       V25       V26       V27       V28  Amount  \\\n0      -0.110474  0.066928  0.128539 -0.189115  0.133558 -0.021053  149.62   \n1       0.101288 -0.339846  0.167170  0.125895 -0.008983  0.014724    2.69   \n2       0.909412 -0.689281 -0.327642 -0.139097 -0.055353 -0.059752  378.66   \n3      -0.190321 -1.175575  0.647376 -0.221929  0.062723  0.061458  123.50   \n4      -0.137458  0.141267 -0.206010  0.502292  0.219422  0.215153   69.99   \n...          ...       ...       ...       ...       ...       ...     ...   \n284802  1.014480 -0.509348  1.436807  0.250034  0.943651  0.823731    0.77   \n284803  0.012463 -1.016226 -0.606624 -0.395255  0.068472 -0.053527   24.79   \n284804 -0.037501  0.640134  0.265745 -0.087371  0.004455 -0.026561   67.88   \n284805 -0.163298  0.123205 -0.569159  0.546668  0.108821  0.104533   10.00   \n284806  0.376777  0.008797 -0.473649 -0.818267 -0.002415  0.013649  217.00   \n\n        Class  \n0           0  \n1           0  \n2           0  \n3           0  \n4           0  \n...       ...  \n284802      0  \n284803      0  \n284804      0  \n284805      0  \n284806      0  \n\n[284807 rows x 31 columns]\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "#导入数据\n",
    "path=r'C:\\Users\\文远\\Desktop\\git_test\\数据处理类\\Kaggle-Data-Credit-Card-Fraud-Detection\\creditcard.csv'\n",
    "df=pd.read_csv(path)\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "            Time         V1         V2        V3        V4        V5  \\\n",
       "0            0.0  -1.359807  -0.072781  2.536347  1.378155 -0.338321   \n",
       "1            0.0   1.191857   0.266151  0.166480  0.448154  0.060018   \n",
       "2            1.0  -1.358354  -1.340163  1.773209  0.379780 -0.503198   \n",
       "3            1.0  -0.966272  -0.185226  1.792993 -0.863291 -0.010309   \n",
       "4            2.0  -1.158233   0.877737  1.548718  0.403034 -0.407193   \n",
       "...          ...        ...        ...       ...       ...       ...   \n",
       "284802  172786.0 -11.881118  10.071785 -9.834783 -2.066656 -5.364473   \n",
       "284803  172787.0  -0.732789  -0.055080  2.035030 -0.738589  0.868229   \n",
       "284804  172788.0   1.919565  -0.301254 -3.249640 -0.557828  2.630515   \n",
       "284805  172788.0  -0.240440   0.530483  0.702510  0.689799 -0.377961   \n",
       "284806  172792.0  -0.533413  -0.189733  0.703337 -0.506271 -0.012546   \n",
       "\n",
       "              V6        V7        V8        V9  ...       V21       V22  \\\n",
       "0       0.462388  0.239599  0.098698  0.363787  ... -0.018307  0.277838   \n",
       "1      -0.082361 -0.078803  0.085102 -0.255425  ... -0.225775 -0.638672   \n",
       "2       1.800499  0.791461  0.247676 -1.514654  ...  0.247998  0.771679   \n",
       "3       1.247203  0.237609  0.377436 -1.387024  ... -0.108300  0.005274   \n",
       "4       0.095921  0.592941 -0.270533  0.817739  ... -0.009431  0.798278   \n",
       "...          ...       ...       ...       ...  ...       ...       ...   \n",
       "284802 -2.606837 -4.918215  7.305334  1.914428  ...  0.213454  0.111864   \n",
       "284803  1.058415  0.024330  0.294869  0.584800  ...  0.214205  0.924384   \n",
       "284804  3.031260 -0.296827  0.708417  0.432454  ...  0.232045  0.578229   \n",
       "284805  0.623708 -0.686180  0.679145  0.392087  ...  0.265245  0.800049   \n",
       "284806 -0.649617  1.577006 -0.414650  0.486180  ...  0.261057  0.643078   \n",
       "\n",
       "             V23       V24       V25       V26       V27       V28  Amount  \\\n",
       "0      -0.110474  0.066928  0.128539 -0.189115  0.133558 -0.021053  149.62   \n",
       "1       0.101288 -0.339846  0.167170  0.125895 -0.008983  0.014724    2.69   \n",
       "2       0.909412 -0.689281 -0.327642 -0.139097 -0.055353 -0.059752  378.66   \n",
       "3      -0.190321 -1.175575  0.647376 -0.221929  0.062723  0.061458  123.50   \n",
       "4      -0.137458  0.141267 -0.206010  0.502292  0.219422  0.215153   69.99   \n",
       "...          ...       ...       ...       ...       ...       ...     ...   \n",
       "284802  1.014480 -0.509348  1.436807  0.250034  0.943651  0.823731    0.77   \n",
       "284803  0.012463 -1.016226 -0.606624 -0.395255  0.068472 -0.053527   24.79   \n",
       "284804 -0.037501  0.640134  0.265745 -0.087371  0.004455 -0.026561   67.88   \n",
       "284805 -0.163298  0.123205 -0.569159  0.546668  0.108821  0.104533   10.00   \n",
       "284806  0.376777  0.008797 -0.473649 -0.818267 -0.002415  0.013649  217.00   \n",
       "\n",
       "        Class  \n",
       "0           0  \n",
       "1           0  \n",
       "2           0  \n",
       "3           0  \n",
       "4           0  \n",
       "...       ...  \n",
       "284802      0  \n",
       "284803      0  \n",
       "284804      0  \n",
       "284805      0  \n",
       "284806      0  \n",
       "\n",
       "[284807 rows x 31 columns]"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Time</th>\n      <th>V1</th>\n      <th>V2</th>\n      <th>V3</th>\n      <th>V4</th>\n      <th>V5</th>\n      <th>V6</th>\n      <th>V7</th>\n      <th>V8</th>\n      <th>V9</th>\n      <th>...</th>\n      <th>V21</th>\n      <th>V22</th>\n      <th>V23</th>\n      <th>V24</th>\n      <th>V25</th>\n      <th>V26</th>\n      <th>V27</th>\n      <th>V28</th>\n      <th>Amount</th>\n      <th>Class</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.0</td>\n      <td>-1.359807</td>\n      <td>-0.072781</td>\n      <td>2.536347</td>\n      <td>1.378155</td>\n      <td>-0.338321</td>\n      <td>0.462388</td>\n      <td>0.239599</td>\n      <td>0.098698</td>\n      <td>0.363787</td>\n      <td>...</td>\n      <td>-0.018307</td>\n      <td>0.277838</td>\n      <td>-0.110474</td>\n      <td>0.066928</td>\n      <td>0.128539</td>\n      <td>-0.189115</td>\n      <td>0.133558</td>\n      <td>-0.021053</td>\n      <td>149.62</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0.0</td>\n      <td>1.191857</td>\n      <td>0.266151</td>\n      <td>0.166480</td>\n      <td>0.448154</td>\n      <td>0.060018</td>\n      <td>-0.082361</td>\n      <td>-0.078803</td>\n      <td>0.085102</td>\n      <td>-0.255425</td>\n      <td>...</td>\n      <td>-0.225775</td>\n      <td>-0.638672</td>\n      <td>0.101288</td>\n      <td>-0.339846</td>\n      <td>0.167170</td>\n      <td>0.125895</td>\n      <td>-0.008983</td>\n      <td>0.014724</td>\n      <td>2.69</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1.0</td>\n      <td>-1.358354</td>\n      <td>-1.340163</td>\n      <td>1.773209</td>\n      <td>0.379780</td>\n      <td>-0.503198</td>\n      <td>1.800499</td>\n      <td>0.791461</td>\n      <td>0.247676</td>\n      <td>-1.514654</td>\n      <td>...</td>\n      <td>0.247998</td>\n      <td>0.771679</td>\n      <td>0.909412</td>\n      <td>-0.689281</td>\n      <td>-0.327642</td>\n      <td>-0.139097</td>\n      <td>-0.055353</td>\n      <td>-0.059752</td>\n      <td>378.66</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1.0</td>\n      <td>-0.966272</td>\n      <td>-0.185226</td>\n      <td>1.792993</td>\n      <td>-0.863291</td>\n      <td>-0.010309</td>\n      <td>1.247203</td>\n      <td>0.237609</td>\n      <td>0.377436</td>\n      <td>-1.387024</td>\n      <td>...</td>\n      <td>-0.108300</td>\n      <td>0.005274</td>\n      <td>-0.190321</td>\n      <td>-1.175575</td>\n      <td>0.647376</td>\n      <td>-0.221929</td>\n      <td>0.062723</td>\n      <td>0.061458</td>\n      <td>123.50</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2.0</td>\n      <td>-1.158233</td>\n      <td>0.877737</td>\n      <td>1.548718</td>\n      <td>0.403034</td>\n      <td>-0.407193</td>\n      <td>0.095921</td>\n      <td>0.592941</td>\n      <td>-0.270533</td>\n      <td>0.817739</td>\n      <td>...</td>\n      <td>-0.009431</td>\n      <td>0.798278</td>\n      <td>-0.137458</td>\n      <td>0.141267</td>\n      <td>-0.206010</td>\n      <td>0.502292</td>\n      <td>0.219422</td>\n      <td>0.215153</td>\n      <td>69.99</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>284802</th>\n      <td>172786.0</td>\n      <td>-11.881118</td>\n      <td>10.071785</td>\n      <td>-9.834783</td>\n      <td>-2.066656</td>\n      <td>-5.364473</td>\n      <td>-2.606837</td>\n      <td>-4.918215</td>\n      <td>7.305334</td>\n      <td>1.914428</td>\n      <td>...</td>\n      <td>0.213454</td>\n      <td>0.111864</td>\n      <td>1.014480</td>\n      <td>-0.509348</td>\n      <td>1.436807</td>\n      <td>0.250034</td>\n      <td>0.943651</td>\n      <td>0.823731</td>\n      <td>0.77</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>284803</th>\n      <td>172787.0</td>\n      <td>-0.732789</td>\n      <td>-0.055080</td>\n      <td>2.035030</td>\n      <td>-0.738589</td>\n      <td>0.868229</td>\n      <td>1.058415</td>\n      <td>0.024330</td>\n      <td>0.294869</td>\n      <td>0.584800</td>\n      <td>...</td>\n      <td>0.214205</td>\n      <td>0.924384</td>\n      <td>0.012463</td>\n      <td>-1.016226</td>\n      <td>-0.606624</td>\n      <td>-0.395255</td>\n      <td>0.068472</td>\n      <td>-0.053527</td>\n      <td>24.79</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>284804</th>\n      <td>172788.0</td>\n      <td>1.919565</td>\n      <td>-0.301254</td>\n      <td>-3.249640</td>\n      <td>-0.557828</td>\n      <td>2.630515</td>\n      <td>3.031260</td>\n      <td>-0.296827</td>\n      <td>0.708417</td>\n      <td>0.432454</td>\n      <td>...</td>\n      <td>0.232045</td>\n      <td>0.578229</td>\n      <td>-0.037501</td>\n      <td>0.640134</td>\n      <td>0.265745</td>\n      <td>-0.087371</td>\n      <td>0.004455</td>\n      <td>-0.026561</td>\n      <td>67.88</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>284805</th>\n      <td>172788.0</td>\n      <td>-0.240440</td>\n      <td>0.530483</td>\n      <td>0.702510</td>\n      <td>0.689799</td>\n      <td>-0.377961</td>\n      <td>0.623708</td>\n      <td>-0.686180</td>\n      <td>0.679145</td>\n      <td>0.392087</td>\n      <td>...</td>\n      <td>0.265245</td>\n      <td>0.800049</td>\n      <td>-0.163298</td>\n      <td>0.123205</td>\n      <td>-0.569159</td>\n      <td>0.546668</td>\n      <td>0.108821</td>\n      <td>0.104533</td>\n      <td>10.00</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>284806</th>\n      <td>172792.0</td>\n      <td>-0.533413</td>\n      <td>-0.189733</td>\n      <td>0.703337</td>\n      <td>-0.506271</td>\n      <td>-0.012546</td>\n      <td>-0.649617</td>\n      <td>1.577006</td>\n      <td>-0.414650</td>\n      <td>0.486180</td>\n      <td>...</td>\n      <td>0.261057</td>\n      <td>0.643078</td>\n      <td>0.376777</td>\n      <td>0.008797</td>\n      <td>-0.473649</td>\n      <td>-0.818267</td>\n      <td>-0.002415</td>\n      <td>0.013649</td>\n      <td>217.00</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>284807 rows × 31 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 17
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "df.shape (284807, 30)\ndf.shape (284807, 29)\n"
     ]
    }
   ],
   "source": [
    "#为了得到train,test\n",
    "####先直接用原始数据\n",
    "y_data=np.array(df['Class'])\n",
    "#去掉没用的time\n",
    "df.drop('Class',axis=1,inplace=True)\n",
    "print('df.shape',df.shape)\n",
    "df.drop('Time',axis=1,inplace=True)\n",
    "print('df.shape',df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "(284807, 29)\n"
     ]
    }
   ],
   "source": [
    "x_data=np.array(df)\n",
    "print(x_data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "(213605, 29) (71202, 29) (213605,) (71202,)\n"
     ]
    }
   ],
   "source": [
    "##最原始的操作，看看效果\n",
    "from sklearn.model_selection import train_test_split\n",
    "##random_state：保证随机不变\n",
    "X_train,X_test,y_train,y_test=train_test_split(x_data,y_data,test_size=0.25,random_state=0)\n",
    "print(X_train.shape,X_test.shape,y_train.shape,y_test.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import metrics  #评价模型的指标\n",
    "\n",
    "#最简单的：LogisticRegression\n",
    "# logreg=LogisticRegression()  ##必须添加最大迭代次数\n",
    "logre=LogisticRegression(max_iter=5000)\n",
    "logre.fit(X_train,y_train)##实例化LogisticRegression模型\n",
    "y_pre=logre.predict(X_test)\n",
    "y_pre_prob=logre.predict_proba(X_test)[:,1]###取第一列，即最大值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "[0 0 0 ... 0 0 0]\n[4.78302116e-04 3.16184741e-04 4.47721943e-04 ... 2.21900961e-04\n 8.01120542e-04 8.91228111e-05]\n(71202,)\n(71202,)\n"
     ]
    }
   ],
   "source": [
    "print(y_pre)\n",
    "print(y_pre_prob)\n",
    "print(y_pre.shape)\n",
    "print(y_pre_prob.shape)\n",
    "# all_prob=logre.predict_proba(X_test)\n",
    "# print(all_prob.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "91\n120\n"
     ]
    }
   ],
   "source": [
    "###预测负样本个数\n",
    "num_one=0\n",
    "for i in y_pre:\n",
    "    if i==1:\n",
    "        num_one+=1\n",
    "print(num_one)\n",
    "###负样本个数\n",
    "num_one=0\n",
    "for i in y_test:\n",
    "    if i==1:\n",
    "        num_one+=1\n",
    "print(num_one)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "[[71071    11]\n [   40    80]]\n原始数据的LogisticRegression:\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x273806f8e80>"
      ]
     },
     "metadata": {},
     "execution_count": 55
    },
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "from sklearn.metrics._classification import confusion_matrix\n",
    "from sklearn.metrics import plot_confusion_matrix\n",
    "cnf_matrix = confusion_matrix(y_test,y_pre)\n",
    "print(cnf_matrix)\n",
    "print(\"原始数据的LogisticRegression:\")\n",
    "plot_confusion_matrix(logre, X_test, y_test)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Confusion Matrix:\n [[71071    11]\n [   40    80]]\nAccuracy:\n 0.999283727985169\nPrecision:\n 0.8791208791208791\nRecall:\n 0.6666666666666666\nAUC:\n 0.9735875467769619\n"
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "# from sklearn.metrics._classification import confusion_matrix\n",
    "# plot_confusion_matrix(logre, X_test, y_test)  \n",
    "#Performance metrics evaluation\n",
    "print(\"Confusion Matrix:\\n\",metrics.confusion_matrix(y_test,y_pre))\n",
    "print(\"Accuracy:\\n\",metrics.accuracy_score(y_test,y_pre))\n",
    "print(\"Precision:\\n\",metrics.precision_score(y_test,y_pre))\n",
    "print(\"Recall:\\n\",metrics.recall_score(y_test,y_pre))\n",
    "print(\"AUC:\\n\",metrics.roc_auc_score(y_test,y_pre_prob))\n",
    "auc=metrics.roc_auc_score(y_test,y_pre_prob)\n",
    "fpr,tpr,thresholds=metrics.roc_curve(y_test,y_pre_prob)\n",
    "plt.plot(fpr,tpr,'b', label='AUC = %0.2f'% auc)\n",
    "plt.plot([0,1],[0,1],'r-.')\n",
    "plt.xlim([-0.2,1.2])\n",
    "plt.ylim([-0.2,1.2])\n",
    "plt.title('original data\\nLogistic Regression')\n",
    "plt.legend(loc='lower right')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.show()"
   ]
  },
  {
   "source": [
    "## 过采样的LogisticRegression"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "(213605, 29) (213605,)\n",
      "过采样后样本的数量：\n",
      "(426466, 29) (426466,)\n",
      "正负样本的数量：\n",
      "213233 : 213233\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape,y_train.shape)\n",
    "#applyting SMOTE to oversample the minority class    ###不平衡样本的处理，选择过采样\n",
    "from imblearn.over_sampling import SMOTE\n",
    "sm=SMOTE(random_state=2)\n",
    "X_sm,y_sm=sm.fit_sample(X_train,y_train)\n",
    "print(\"过采样后样本的数量：\")\n",
    "print(X_sm.shape,y_sm.shape)\n",
    "print(\"正负样本的数量：\")\n",
    "print(len(y_sm[y_sm==1]),\":\",len(y_sm[y_sm==0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([0, 0, 0, ..., 1, 1, 1], dtype=int64)"
      ]
     },
     "metadata": {},
     "execution_count": 36
    }
   ],
   "source": [
    "y_sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([[ 2.04716304e+00,  1.07986610e-01, -1.80651506e+00, ...,\n",
       "        -8.50246560e-02, -8.42920010e-02,  7.70000000e-01],\n",
       "       [ 1.28240449e+00,  4.59864448e-01, -3.72285844e-01, ...,\n",
       "        -1.01678630e-02, -5.33248700e-03,  1.79000000e+00],\n",
       "       [-4.14862511e-01,  1.20257800e-02,  1.35638604e+00, ...,\n",
       "         2.37627707e-01,  2.04342556e-01,  5.95000000e+00],\n",
       "       ...,\n",
       "       [-3.60959842e+00, -2.43416707e+00, -3.25602679e+00, ...,\n",
       "         4.45716809e-01, -3.85185390e-01,  3.78521888e+01],\n",
       "       [-1.01124549e+00,  8.38265047e-01, -2.44327267e-01, ...,\n",
       "         4.33853948e-01,  2.47085357e-01,  3.23294681e+02],\n",
       "       [-1.92745282e+00,  1.82762057e+00, -7.01949468e+00, ...,\n",
       "         1.27770726e+00,  8.19081152e-01,  5.12250000e+02]])"
      ]
     },
     "metadata": {},
     "execution_count": 35
    }
   ],
   "source": [
    "X_sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import metrics  #评价模型的指标\n",
    "\n",
    "#最简单的：LogisticRegression\n",
    "# logreg=LogisticRegression()  ##必须添加最大迭代次数\n",
    "logre2=LogisticRegression(max_iter=5000)\n",
    "logre2.fit(X_sm,y_sm)##实例化LogisticRegression模型\n",
    "y_pre2=logre2.predict(X_test)\n",
    "y_pre_prob2=logre2.predict_proba(X_test)[:,1]###取第一列，即最大值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "91\n120\n"
     ]
    }
   ],
   "source": [
    "###预测负样本个数\n",
    "num_one=0\n",
    "for i in y_pre:\n",
    "    if i==1:\n",
    "        num_one+=1\n",
    "print(num_one)\n",
    "###负样本个数\n",
    "num_one=0\n",
    "for i in y_test:\n",
    "    if i==1:\n",
    "        num_one+=1\n",
    "print(num_one)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "[[69876  1206]\n [   13   107]]\n过采样后的混淆矩阵：\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x2738062a710>"
      ]
     },
     "metadata": {},
     "execution_count": 59
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 2 Axes>",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "from sklearn.metrics._classification import confusion_matrix\n",
    "from sklearn.metrics import plot_confusion_matrix\n",
    "cnf_matrix2 = confusion_matrix(y_test,y_pre2)\n",
    "print(cnf_matrix2)\n",
    "print(\"过采样后的混淆矩阵：\")\n",
    "plot_confusion_matrix(logre2, X_test, y_test)  "
   ]
  },
  {
   "source": [
    "from sklearn import metrics\n",
    "# from sklearn.metrics._classification import confusion_matrix\n",
    "# plot_confusion_matrix(logre, X_test, y_test)  \n",
    "#Performance metrics evaluation\n",
    "print(\"Confusion Matrix:\\n\",metrics.confusion_matrix(y_test,y_pre2))\n",
    "print(\"Accuracy:\\n\",metrics.accuracy_score(y_test,y_pre2))\n",
    "print(\"Precision:\\n\",metrics.precision_score(y_test,y_pre2))\n",
    "print(\"Recall:\\n\",metrics.recall_score(y_test,y_pre2))\n",
    "print(\"AUC:\\n\",metrics.roc_auc_score(y_test,y_pre_prob2))\n",
    "auc=metrics.roc_auc_score(y_test,y_pre_prob2)\n",
    "fpr,tpr,thresholds=metrics.roc_curve(y_test,y_pre_prob2)\n",
    "plt.plot(fpr,tpr,'b', label='AUC = %0.2f'% auc)\n",
    "plt.plot([0,1],[0,1],'r-.')\n",
    "plt.xlim([-0.2,1.2])\n",
    "plt.ylim([-0.2,1.2])\n",
    "plt.title('Over_Sample\\nLogistic Regression')\n",
    "plt.legend(loc='lower right')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.show()"
   ],
   "cell_type": "code",
   "metadata": {},
   "execution_count": 60,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Confusion Matrix:\n [[69876  1206]\n [   13   107]]\nAccuracy:\n 0.982879694390607\nPrecision:\n 0.08149276466108149\nRecall:\n 0.8916666666666667\nAUC:\n 0.9765286335968786\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ]
  },
  {
   "source": [
    "## 下采样的LogisticRegression"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "(284807, 29)\n[[-1.35980713e+00 -7.27811730e-02  2.53634674e+00 ...  1.33558377e-01\n  -2.10530530e-02  1.49620000e+02]\n [ 1.19185711e+00  2.66150712e-01  1.66480113e-01 ... -8.98309900e-03\n   1.47241690e-02  2.69000000e+00]\n [-1.35835406e+00 -1.34016307e+00  1.77320934e+00 ... -5.53527940e-02\n  -5.97518410e-02  3.78660000e+02]\n ...\n [ 1.91956501e+00 -3.01253846e-01 -3.24963981e+00 ...  4.45477200e-03\n  -2.65608290e-02  6.78800000e+01]\n [-2.40440050e-01  5.30482513e-01  7.02510230e-01 ...  1.08820735e-01\n   1.04532821e-01  1.00000000e+01]\n [-5.33412522e-01 -1.89733337e-01  7.03337367e-01 ... -2.41530900e-03\n   1.36489140e-02  2.17000000e+02]]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "## 缺少标准化\n",
    "# 1、preprocessing.StandardScaler.fit\n",
    "# 用于计算训练数据的均值和方差， 后面就会用均值和方差来转换训练数据\n",
    "\n",
    "# 2、preprocessing.StandardScaler.fit_transform\n",
    "# 不仅计算训练数据的均值和方差，还会基于计算出来的均值和方差来转换训练数据，从而把数据转换成标准的正太分布\n",
    "\n",
    "# 3、preprocessing.StandardScaler.transform\n",
    "# 很显然，它只是进行转换，只是把训练数据转换成标准的正态分布\n",
    "x_data=np.array(df)\n",
    "print(x_data.shape)\n",
    "print(x_data)\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "standard_data=StandardScaler().fit_transform(x_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([[-0.69424232, -0.04407492,  1.6727735 , ...,  0.33089162,\n",
       "        -0.06378115,  0.24496426],\n",
       "       [ 0.60849633,  0.16117592,  0.1097971 , ..., -0.02225568,\n",
       "         0.04460752, -0.34247454],\n",
       "       [-0.69350046, -0.81157783,  1.16946849, ..., -0.13713686,\n",
       "        -0.18102083,  1.16068593],\n",
       "       ...,\n",
       "       [ 0.98002374, -0.18243372, -2.14320514, ...,  0.01103672,\n",
       "        -0.0804672 , -0.0818393 ],\n",
       "       [-0.12275539,  0.32125034,  0.46332013, ...,  0.26960398,\n",
       "         0.31668678, -0.31324853],\n",
       "       [-0.27233093, -0.11489898,  0.46386564, ..., -0.00598394,\n",
       "         0.04134998,  0.51435531]])"
      ]
     },
     "metadata": {},
     "execution_count": 53
    }
   ],
   "source": [
    "# standard_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "<class 'numpy.ndarray'>\n<class 'numpy.ndarray'>\n(284807, 29)\n(284807,)\n(284807, 1)\n"
     ]
    }
   ],
   "source": [
    "print(type(standard_data))\r\n",
    "print(type(y_data))\r\n",
    "print(standard_data.shape)\r\n",
    "print(y_data.shape)\r\n",
    "print(y_data.reshape(-1,1).shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "(284807, 30)\n492\n(984, 30)\n"
     ]
    }
   ],
   "source": [
    "###下采样加交叉验证\n",
    "## 从X_train,y_train中选出1个数的0\n",
    "## 获取y_data=1的索引\n",
    "all_data=np.hstack((standard_data,y_data.reshape(-1,1)))###一定要先和成tuple\n",
    "print(all_data.shape)\n",
    "# print(all_data)\n",
    "data_fraud=all_data[all_data[:,-1] == 1]\n",
    "num_records_fraud = len(all_data[all_data[:,-1] == 1])\n",
    "print(num_records_fraud)\n",
    "# print(data_fraud)\n",
    "data_nomal=all_data[all_data[:,-1] == 0]\n",
    "num_records_nomal = len(data_nomal)\n",
    "\n",
    "random_normal_indexes = np.random.choice(np.arange(num_records_nomal), num_records_fraud, replace=False)###随机抽 num_records_fraud个正常样本,replace=False即不放回抽样\n",
    "# print(random_normal_indexes)\n",
    "\n",
    "down_data=np.vstack((data_fraud,data_nomal[random_normal_indexes,:]))\n",
    "print(down_data.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
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     ]
    }
   ],
   "source": [
    "print(down_data[:,-1])\n",
    "print(len(down_data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "[[-0.62780094  1.18457726 -0.56138278 ...  0.22815041  0.3301235\n   1.        ]\n [-0.7076365  -0.8078994  -0.3583454  ... -1.21556829 -0.25727532\n   0.        ]\n [-0.35667676  0.32106868  0.60420447 ...  0.0232409  -0.24987886\n   0.        ]\n ...\n [-2.07485607  1.87786848 -0.78383919 ... -1.91044616  0.36506677\n   1.        ]\n [ 0.5544154  -0.15678462  0.61366334 ...  0.07792169 -0.23348671\n   0.        ]\n [-0.77564173  1.39798008 -0.99165507 ... -0.63111184 -0.34923131\n   1.        ]]\n(738, 29) (246, 29) (738,) (246,)\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# import numpy as np\n",
    "y_pre_list=np.array([])\n",
    "y_pre_prob_list=np.array([])\n",
    "logre3=LogisticRegression(max_iter=5000)\n",
    "random_index=np.random.choice(np.arange(len(down_data)),len(down_data),replace=False)\n",
    "# print(random_index)\n",
    "down_random_data=down_data[random_index,:]\n",
    "print(down_random_data)\n",
    "# logre2.fit(X_sm,y_sm)##实例化LogisticRegression模型\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "##random_state：保证随机不变\n",
    "X_train1,X_test1,y_train1,y_test1=train_test_split(down_random_data[:,:-1],down_random_data[:,-1],test_size=0.25,random_state=0)\n",
    "print(X_train1.shape,X_test1.shape,y_train1.shape,y_test1.shape)\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import metrics  #评价模型的指标\n",
    "\n",
    "#最简单的：LogisticRegression\n",
    "# logreg=LogisticRegression()  ##必须添加最大迭代次数\n",
    "logre3=LogisticRegression(max_iter=5000)\n",
    "logre3.fit(X_train1,y_train1)##实例化LogisticRegression模型\n",
    "y_pre3=logre3.predict(X_test1)\n",
    "y_pre_prob3=logre3.predict_proba(X_test1)[:,1]###取第一列，即最大值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "[[123   6]\n [  6 111]]\n下采样的混淆矩阵\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x27380643860>"
      ]
     },
     "metadata": {},
     "execution_count": 62
    },
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "from sklearn.metrics._classification import confusion_matrix\n",
    "from sklearn.metrics import plot_confusion_matrix\n",
    "cnf_matrix3 = confusion_matrix(y_test1,y_pre3)\n",
    "print(cnf_matrix3)\n",
    "print(\"下采样的混淆矩阵\")\n",
    "plot_confusion_matrix(logre3, X_test1, y_test1)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Confusion Matrix:\n [[123   6]\n [  6 111]]\nAccuracy:\n 0.9512195121951219\nPrecision:\n 0.9487179487179487\nRecall:\n 0.9487179487179487\nAUC:\n 0.9943019943019942\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "# from sklearn.metrics._classification import confusion_matrix\n",
    "# plot_confusion_matrix(logre, X_test, y_test)  \n",
    "#Performance metrics evaluation\n",
    "print(\"Confusion Matrix:\\n\",metrics.confusion_matrix(y_test1,y_pre3))\n",
    "print(\"Accuracy:\\n\",metrics.accuracy_score(y_test1,y_pre3))\n",
    "print(\"Precision:\\n\",metrics.precision_score(y_test1,y_pre3))\n",
    "print(\"Recall:\\n\",metrics.recall_score(y_test1,y_pre3))\n",
    "print(\"AUC:\\n\",metrics.roc_auc_score(y_test1,y_pre_prob3))\n",
    "auc=metrics.roc_auc_score(y_test1,y_pre_prob3)\n",
    "fpr,tpr,thresholds=metrics.roc_curve(y_test1,y_pre_prob3)\n",
    "plt.plot(fpr,tpr,'b', label='AUC = %0.2f'% auc)\n",
    "plt.plot([0,1],[0,1],'r-.')\n",
    "plt.xlim([-0.2,1.2])\n",
    "plt.ylim([-0.2,1.2])\n",
    "plt.title('Down_Sample\\nLogistic Regression')\n",
    "plt.legend(loc='lower right')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "(426466, 29)\n(426466,)\n(426466, 1)\n(426466,)\n(319849, 29) (106617, 29) (319849,) (106617,)\n"
     ]
    }
   ],
   "source": [
    "#发现召回率还是挺不算高，加上交叉验证\n",
    "#########    原来的FP挺大：1206个\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.linear_model import LogisticRegressionCV # 带有正则化参数C的粒度\n",
    "from sklearn.model_selection import cross_val_score # 交叉验证\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "# X_sm,y_sm=sm.fit_sample(X_train,y_train)\n",
    "standard_X_sm=StandardScaler().fit_transform(X_sm)\n",
    "print(standard_X_sm.shape)\n",
    "print(y_sm.shape)\n",
    "y_sm=y_sm.reshape(-1,1)\n",
    "print(y_sm.shape)\n",
    "y_sm=y_sm.reshape(-1)\n",
    "print(y_sm.shape)\n",
    "# over_data=np.vstack((standard_X_sm,y_sm))\n",
    "# print(over_data)\n",
    "# print(over_data.shape)\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "##random_state：保证随机不变\n",
    "X_train2,X_test2,y_train2,y_test2=train_test_split(standard_X_sm,y_sm,test_size=0.25,random_state=0)\n",
    "print(X_train2.shape,X_test2.shape,y_train2.shape,y_test2.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegressionCV # 带有正则化参数C的粒度\n",
    "# 大量样本（30W+）、高维度（29），L1正则 --> 可选用saga优化求解器(0.19版本新功能)\n",
    "# LogisticRegressionCV比GridSearchCV快     solver：逻辑回归损失函数的优化方法。\n",
    "lgrecv_L2 = LogisticRegressionCV(Cs=1, cv = 10, penalty='l2', solver='saga')\n",
    "####  关于 Cs参数，Cs：也就是正则化系数，相当于 LogisticRegression里的参数C。\n",
    "lgrecv_L2.fit(X_train2, y_train2) \n",
    "y_pre5=lgrecv_L2.predict(X_test2)\n",
    "y_pre_prob5=lgrecv_L2.predict_proba(X_test2)[:,1]###取第一列，即最大值\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "[[53197   319]\n [ 5734 47367]]\n过采样+交叉验证的混淆矩阵\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x27385f13b70>"
      ]
     },
     "metadata": {},
     "execution_count": 65
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 2 Axes>",
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DZzRT1lXAVbtIn0GRPduCnmiRdCDZPJ/Ge4kRcVsxH2Rm5UKdc5WcBpKuAI4jC4ozyCZC/glwUDTrrDr5Y36nAycAyyLiU8BBZKM+ZtZZ1Re4dUCFdJ83RUS9pO2S+pJNshzZ0kVmVqY66yKzOeamYfOfk41IrweeKGWlzKx9a6XR53apxaAYERel3Z9KehDom0aNzKyz6oxBUdKh+c5FxF9LUyUzs7aTr6X4wzznGlayaFWLFvbnlGNPazmjtRtfrvbDTR3JpZM2tUo5nbL7HBETdmdFzKyDCFp8hK8jK2jytplZE52xpWhm1pxO2X02M2tWGQfFQlbelqRPSPrPdDxK0lse1jazTqSTv/f5BrKFH89Jx+vIVuI2s05IUfjWERXSfT4yIg6V9AxARKxueOWgmXVSnXz0eVtaBDIgWy2XDvuot5m1ho7aCixEId3n68leKLOHpKvIlg37r5LWyszatzK+p1jIs8+3S5pHtnyYgFMj4oWS18zM2qcOfL+wEIUsMjsK2Ajcl5sWEa+WsmJm1o515qAI3M+OF1h1B8YAC4EDSlgvM2vHVMajCoV0n9+be5xWz7momexmZh1a0U+0RMRfJR1ZisqYWQfRmbvPkr6Sc1gBHAq8VrIamVn71tkHWoA+Ofvbye4x/q401TGzDqGzBsU0abtPRFy2m+pjZh1BZwyKkrpExHZJx+zOCplZ+yY67+jz02T3D/8maTrwW2BDw8mI+H2J62Zm7ZHvKdIdWEX2TpaG+YoBOCiadVadNCjukUaen2dHMGxQxr8SM2tRGUeAfEGxEuhN02DYoIx/JWbWks7afX49Iq7cbTUxs46jkwbF8l1F0szevijv0ed86ymesNtqYWYdSyuspyhppKQ/SFogab6kS1P6QEmzJC1KPwekdEm6XlK1pGfTOgwNZU1O+RdJmpyTfpik59I110tqsbHXbFCMiNqWLjazzqmV3tGyHfj3iBgHHAVcLGkcMAWYHRFjgdnpGOBkYGzaLgRuhCyIAlcARwJHAFc0BNKU5zM5101sqVKFrLxtZtZUK7QUI+L1iPhr2l8HvAAMByYBt6ZstwKnpv1JwG2ReRLoL2kYcBIwKyJqI2I1MAuYmM71jYgnIyKA23LKapbf+2xmxSnuVQODJc3NOZ4aEVN3ziRpNHAI8BQwNCJeT6eWAUPT/nBgSc5lNSktX3rNLtLzclA0s6KIoqbkrIyI8XnLk3qTLTLzpYhYm3vbLyJC2r0TgNx9NrOitdZ7nyVVkQXE23MeHX4jdX1JP5en9KXAyJzLR6S0fOkjdpGel4OimRWvdUafBdwMvBAR1+Scmg40jCBPBu7NST8/jUIfBaxJ3eyZwImSBqQBlhOBmencWklHpc86P6esZrn7bGbFa50O7THAJ4HnJP0tpX0DuBq4S9IFwCvAmencDOAUoJrsZXqfgmymjKTvAHNSvitzZs9cBNwC9AAeSFteDopmVpxWWiUnIv5E8w+JvGWedBpBvriZsqYB03aRPhc4sJh6OSiaWfE66WN+Zma7VM6P+TkomlnROusqOWZmb1Xc5O0Ox0HRzIrnoGhmlinyiZYOx0HRzIqm+vKNig6KZlYc31M0M2vK3Wczs1wOimZmO7ilaGaWy0HRzCwp87f5OSiaWVE8T9HMbGdRvlHRQdHMiuaWor3FL+6cyaZNVdTVQX1dBZdeeBxTvjWH4SPXAdC79zbWr6/iixccz7vfs5ovXvYMABLc/ov9eeLxvRrLqqgIfjT1UVat7M63phzdJt+nXNXXwa9PHUPvPbdx6s9ruPPsvdm2IXsLx8ZVlez5vs187Kc1vDSrN3+5bgiqAFUGx33zDYaP3wTA2te6MOvrw1i/rAqAU29eQr8R25otq+x58vbbI2ka8FFgeUQUtfJtRzHl0mNYu6Zb4/HV3zq8cf/TFz/HhvXZH9Eri/tw6YXHUV9XwYBBm/nJtEd46i97Ul+X/UFNOv0llrzSh569tu3eL9AJPHPLQAbuu4Wt67Pf9Vl3vNJ47r6Lh7PPh9YDMPL9G/jEh9YjwYp/dOP+S4bzrw8tBmDmZXtxxEWr2PsDG9i6QSi92ai5sjqDch5oKeWLq24BJpaw/HYs+OCE1/jj7OxFYlu2dGkMgF271hGxYwX2QUM2cfjRy5h5/95tUtNytu71Lrz8aG8OPPPNt5zbsq6CJU/0Yp8PZS37rr2ChjdrbttY0bi/alFX6uvE3h/Y0JivqkfkLaszUH1hW0dUspZiRDyWXnBdlgLx3R/+hQh4YPoYHrxvdOO5Aw9axZu13Xitpndj2n7vqeVLU55hj6Eb+cFVhzUGyc9+8Tmm3XggPXq6ldjaHv3uUD74teWNrcRcLz3ch5FHb6Bbnx1/udUP9eFPPxjCxlVdOPXn2bvVV/+zK9361nHfRcNZs6Qro47ZwAcuX05FZf6yylpQ1gMtbf6KU0kXSporae7Wuk1tXZ2CXX7xB7nk0xP4z8vfz0dPW8yBB61sPPcvJ9Tw6OzhTfIvfGEgn598Al/67HGc+YkXqepaxxFHL+PN1d2ofrH/bq59+Vv8SG96Dqpj6IGbd3l+4X192f9/rW2Stu+J6/jXhxbzsRuX8JfrhgBQv10sndOTD05Zzrl3v8yaJVUs+F2/Fssqd6313uf2qM2DYkRMjYjxETG+a2WPtq5OwVatzOq65s1uPPH4MN79ntUAVFTW8/5jX+exR0bs8rolr/Rh86YujB6zlnHvXcVRx7zOL+6cydeumMv7Dl3JZd+cu9u+Qzl7bV4PFs/uzc3/sg8zvjScJU/04oGvZINbm2orWfZsd8ZM2PU9wBFHbGLNkio21VbSZ89tDHnPZvqP2kZFF9jnQ+tYPr97Y96WyipbrfDe5/bKo89vQ7fu26lQsGlTFd26b+eQw1fwm1v2A+CQw1ZQ82pvVq3YEeCHDtvAiuU9qK+rYI+hGxkxaj1vLOvJLVMP4JapBwDw3oNX8L/PruYH3x3fJt+p3Hzg8hV84PIVACx5sifzbh7Iyde8BsCLD/ZhzIT1dOm246/2zX9W0W/vbUjwxvPdqdsqug+oY2i/Orasq2Tjqkp6DqpjyZO9mrQ+d1VWufPkbXuLAQO28M2rngKgsjJ49OERzHt6KADHnlDDHx9u2ko84L2rOOO8RWzfLiLEDde8r8mote1eL/5PXw7/7KomaYtm9mXB3f2orAq6dKvnIz9aigSqhGOnLOd3548iAoYeuJn3nrU6b1llL6KsF5lVlOiGqaTfAMcBg4E3gCsi4uZ81/TrvmccPer8ktTHSuOSB2e0dRWsCJdOeolFz21q7gX0BenTf0QccuylBeV9/L6vzouIDtX9KeXo8zmlKtvM2pa7z2ZmDQIo4+6zg6KZFa98Y6KDopkVz91nM7Mc5Tz67KBoZsXpwBOzC+GgaGZFySZvl29UbPPH/MysA6ovcGuBpGmSlkt6PidtoKRZkhalnwNSuiRdL6la0rOSDs25ZnLKv0jS5Jz0wyQ9l665XlKLczQdFM2saIooaCvALbx1icEpwOyIGAvMTscAJwNj03YhcCNkQRS4AjgSOAK4oiGQpjyfybmuxeUMHRTNrDiFLgZRQEyMiMeA2p2SJwG3pv1bgVNz0m+LzJNAf0nDgJOAWRFRGxGrgVnAxHSub0Q8Gdmje7fllNUs31M0syIV9ezzYEm5Sz9NjYipLVwzNCJeT/vLgKFpfziwJCdfTUrLl16zi/S8HBTNrHiFD7SsfCfPPkdESLt3VqS7z2ZWnCj56wjeSF1f0s/lKX0pMDIn34iUli99xC7S83JQNLPiRRS2vT3TgYYR5MnAvTnp56dR6KOANambPRM4UdKANMByIjAznVsr6ag06nx+TlnNcvfZzIrXSh3a3CUGJdWQjSJfDdwl6QLgFeDMlH0GcApQDWwEPgUQEbWSvgPMSfmujIiGwZuLyEa4ewAPpC0vB0UzK5rqW+clXXmWGDxhF3kDuLiZcqYB03aRPhco6hXLDopmVpygoInZHZWDopkVRRQ8MbtDclA0s+I5KJqZ5XBQNDNLfE/RzKyp1hp9bo8cFM2sSO9oYna756BoZsUJHBTNzJoo396zg6KZFc/zFM3McjkompklEVBXvv1nB0UzK55bimZmORwUzcySAAp/R0uH46BoZkUKCN9TNDPLBB5oMTNrwvcUzcxyOCiamTXwghBmZjsE4KXDzMxyuKVoZtbAj/mZme0QEJ6naGaWw0+0mJnl8D1FM7MkwqPPZmZNuKVoZtYgiLq6tq5EyTgomllxvHSYmdlOPCXHzCwTQLilaGaWhBeZNTNropwHWhTtaGhd0grglbauRwkMBla2dSWsKOX632zviBjyTgqQ9CDZ76cQKyNi4jv5vN2tXQXFciVpbkSMb+t6WOH836zzqmjrCpiZtScOimZmORwUd4+pbV0BK5r/m3VSvqdoZpbDLUUzsxwOimZmORwUS0jSREkLJVVLmtLW9bGWSZomabmk59u6LtY2HBRLRFIl8BPgZGAccI6kcW1bKyvALUCHmmxsrctBsXSOAKojYnFEbAXuACa1cZ2sBRHxGFDb1vWwtuOgWDrDgSU5xzUpzczaMQdFM7McDoqlsxQYmXM8IqWZWTvmoFg6c4CxksZI6gqcDUxv4zqZWQscFEskIrYDXwBmAi8Ad0XE/LatlbVE0m+AJ4D9JNVIuqCt62S7lx/zMzPL4ZaimVkOB0UzsxwOimZmORwUzcxyOCiameVwUOxAJNVJ+puk5yX9VlLPd1DWLZJOT/s35VusQtJxkt7/Nj7jn5Le8ta35tJ3yrO+yM/6lqTLiq2j2c4cFDuWTRFxcEQcCGwFPpd7UtLbeo93RHw6IhbkyXIcUHRQNOuIHBQ7rseBfVMr7nFJ04EFkiol/V9JcyQ9K+mzAMr8OK3v+DCwR0NBkh6VND7tT5T0V0l/lzRb0miy4Pvl1Er9oKQhkn6XPmOOpGPStYMkPSRpvqSbALX0JSTdI2leuubCnc5dm9JnSxqS0vaR9GC65nFJ+7fKb9MseVstC2tbqUV4MvBgSjoUODAiXk6BZU1EHC6pG/BnSQ8BhwD7ka3tOBRYAEzbqdwhwM+BY1NZAyOiVtJPgfUR8YOU79fAtRHxJ0mjyJ7aeQ9wBfCniLhS0keAQp4G+bf0GT2AOZJ+FxGrgF7A3Ij4sqT/TGV/geyFUp+LiEWSjgRuAI5/G79Gs11yUOxYekj6W9p/HLiZrFv7dES8nNJPBN7XcL8Q6AeMBY4FfhMRdcBrkh7ZRflHAY81lBURza0r+CFgnNTYEOwrqXf6jI+na++XtLqA73SJpNPS/shU11VAPXBnSv8V8Pv0Ge8Hfpvz2d0K+AyzgjkodiybIuLg3IQUHDbkJgFfjIiZO+U7pRXrUQEcFRGbd1GXgkk6jizAHh0RGyU9CnRvJnukz31z59+BWWvyPcXyMxP4vKQqAEnvltQLeAw4K91zHAZM2MW1TwLHShqTrh2Y0tcBfXLyPQR8seFA0sFp9zHg3JR2MjCghbr2A1angLg/WUu1QQXQ0No9l6xbvhZ4WdIZ6TMk6aAWPsOsKA6K5ecmsvuFf00vX/oZWY/gbmBROncb2UowTUTECuBCsq7q39nRfb0POK1hoAW4BBifBnIWsGMU/NtkQXU+WTf61Rbq+iDQRdILwNVkQbnBBuCI9B2OB65M6ecBF6T6zceveLBW5lVyzMxyuKVoZpbDQdHMLIeDoplZDgdFM7McDopmZjkcFM3Mcjgompnl+P9bxxQiOrOCUgAAAABJRU5ErkJggg==\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "from sklearn.metrics._classification import confusion_matrix\n",
    "cnf_matrix5 = confusion_matrix(y_test2,y_pre5)\n",
    "print(cnf_matrix5)\n",
    "print(\"过采样+交叉验证的混淆矩阵\")\n",
    "plot_confusion_matrix(lgrecv_L2, X_test2, y_test2) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Confusion Matrix:\n [[53197   319]\n [ 5734 47367]]\nAccuracy:\n 0.9432266899274975\nPrecision:\n 0.9933104055697689\nRecall:\n 0.8920170994896518\nAUC:\n 0.9879326762036705\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "# from sklearn.metrics._classification import confusion_matrix\n",
    "# plot_confusion_matrix(logre, X_test, y_test)  \n",
    "#Performance metrics evaluation\n",
    "print(\"Confusion Matrix:\\n\",metrics.confusion_matrix(y_test2,y_pre5))\n",
    "print(\"Accuracy:\\n\",metrics.accuracy_score(y_test2,y_pre5))\n",
    "print(\"Precision:\\n\",metrics.precision_score(y_test2,y_pre5))\n",
    "print(\"Recall:\\n\",metrics.recall_score(y_test2,y_pre5))\n",
    "print(\"AUC:\\n\",metrics.roc_auc_score(y_test2,y_pre_prob5))\n",
    "auc=metrics.roc_auc_score(y_test2,y_pre_prob5)\n",
    "fpr,tpr,thresholds=metrics.roc_curve(y_test2,y_pre_prob5)\n",
    "plt.plot(fpr,tpr,'b', label='AUC = %0.2f'% auc)\n",
    "plt.plot([0,1],[0,1],'r-.')\n",
    "plt.xlim([-0.2,1.2])\n",
    "plt.ylim([-0.2,1.2])\n",
    "plt.title('Over_Sample+CrossOver_10_fold\\nLogistic Regression')\n",
    "plt.legend(loc='lower right')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#自己做5次交叉验证\n",
    "# for i in range(5):\n",
    "#     TempX=down_random_data[:,:-1]\n",
    "#     TempY=down_random_data[:,-1]\n",
    "#     print('TempX.shape0',TempX.shape)\n",
    "#     print('TempY.shape0',TempY.shape)\n",
    "#     count_num=int(len(TempY)/6)\n",
    "#     start=count_num*i\n",
    "#     end=count_num*(i+1)\n",
    "#     X_test= TempX[start:end,:]  #记录第i个模型对应的一个测试集 X_test\n",
    "#     Y_test=y_data[start:end]  #选出第i个模型对应的一个测试集 Y_test\n",
    "#     #去掉所有的准备test的样本\n",
    "#     for j in range(start,end):\n",
    "#         # print(j)\n",
    "#         TempX=np.delete(TempX,j,axis=0)\n",
    "#         TempY=np.delete(TempY,j,axis=0)#将temp_xy中删掉第i行，及去掉一个样本\n",
    "#     print('TempX.shape1',TempX.shape)\n",
    "#     print('TempY.shape1',TempY.shape)\n",
    "#     logre3.fit(TempX,TempY) #用剩下来的984-164个样本来训练模型\n",
    "#     y_pre3=logre3.predict(X_test) #用第i个模型对应的一个测试集 Y_test\n",
    "#     y_pre_prob3=logre3.predict_proba(X_test)[:,1]###取第一列，即最大值\n",
    "#     # print(result.shape)\n",
    "#     # print(type(result))\n",
    "#     from sklearn.metrics._classification import confusion_matrix\n",
    "#     cnf_matrix3 = confusion_matrix(Y_test,y_pre3)\n",
    "#     print(cnf_matrix3)\n",
    "#     plot_confusion_matrix(logre3, X_test, Y_test)  \n",
    "#     # np.vstack((y_pre_list,y_pre3.reshape(-1,1)))\n",
    "#     # np.vstack((y_pre_prob_list,y_pre_prob3.reshape(-1,1)))\n",
    "#     print(y_pre3.shape)\n",
    "#     print(y_pre_prob3.shape)\n",
    "# print(y_pre_list.shape)\n",
    "# print(y_pre_prob_list.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "from sklearn.linear_model import LogisticRegressionCV\n",
    "\n",
    "Cs = [1, 10,100,1000]\n",
    "\n",
    "# 大量样本（30W+）、高维度（29），L1正则 --> 可选用saga优化求解器(0.19版本新功能)\n",
    "# LogisticRegressionCV比GridSearchCV快     solver：逻辑回归损失函数的优化方法。\n",
    "\n",
    "# lgrecv_L1 = LogisticRegressionCV(Cs=Cs, cv = 10, penalty='l1', solver='liblinear')\n",
    "# lgrecv_L2 = LogisticRegressionCV(Cs=Cs, cv = 10, penalty='l2', solver='saga')\n",
    "lgrecv_L1 = LogisticRegressionCV(Cs=Cs, cv = 10, penalty='l1', solver='saga')\n",
    "####  关于 Cs参数，Cs：也就是正则化系数，相当于 LogisticRegression里的参数C。\n",
    "lgrecv_L1.fit(X_train2, y_train2) \n",
    "y_pre4=lgrecv_L1.predict(X_test2)\n",
    "y_pre_prob4=lgrecv_L1.predict_proba(X_test2)[:,1]###取第一列，即最大值\n",
    "from sklearn.metrics._classification import confusion_matrix\n",
    "cnf_matrix4 = confusion_matrix(y_test2,y_pre4)\n",
    "print(cnf_matrix4)\n",
    "plot_confusion_matrix(lgrecv_L1, X_test2, y_test2)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "(426466, 29)\n",
      "(426466,)\n",
      "(426466, 1)\n",
      "(426466,)\n",
      "(319849, 29) (106617, 29) (319849,) (106617,)\n",
      "[0.95719869 0.95969986 0.95854307 0.95801157 0.95916836 0.95810536\n",
      " 0.95982492 0.95907457 0.96007504 0.95957354]\n",
      "0.9589274959310202\n"
     ]
    }
   ],
   "source": [
    "#发现召回率还是挺不算高，加上交叉验证\n",
    "#########    原来的FP挺大：1206个\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.linear_model import LogisticRegressionCV # 带有正则化参数C的粒度\n",
    "from sklearn.model_selection import cross_val_score # 交叉验证\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "# X_sm,y_sm=sm.fit_sample(X_train,y_train)\n",
    "standard_X_sm=StandardScaler().fit_transform(X_sm)\n",
    "print(standard_X_sm.shape)\n",
    "print(y_sm.shape)\n",
    "y_sm=y_sm.reshape(-1,1)\n",
    "print(y_sm.shape)\n",
    "y_sm=y_sm.reshape(-1)\n",
    "print(y_sm.shape)\n",
    "# over_data=np.vstack((standard_X_sm,y_sm))\n",
    "# print(over_data)\n",
    "# print(over_data.shape)\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "##random_state：保证随机不变\n",
    "X_train2,X_test2,y_train2,y_test2=train_test_split(standard_X_sm,y_sm,test_size=0.25,random_state=0)\n",
    "print(X_train2.shape,X_test2.shape,y_train2.shape,y_test2.shape)\n",
    "\n",
    "# # logisticregressioncv 类用参数指定网格搜索对模型的正则化参数C的粒度\n",
    "# model_cv = LogisticRegressionCV(10)\n",
    "# model_cv.fit(X_train2, y_train2)\n",
    " \n",
    "# 交叉验证带有四个不重叠训练数据的模型\n",
    "logre4 = LogisticRegression(max_iter=5000)\n",
    "scores = cross_val_score(logre4, X_train2, y_train2, cv = 10) # 做10次交叉验证\n",
    "print(scores)\n",
    "print(scores.mean())\n",
    "\n",
    "# logre4=LogisticRegression(max_iter=5000)\n",
    "# logre4.fit(X_sm,y_sm)##实例化LogisticRegression模型\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "[0.95719869 0.95969986 0.95854307 0.95801157 0.95916836 0.95810536\n 0.95982492 0.95907457 0.96007504 0.95957354]\n0.9589274959310202\n"
     ]
    }
   ],
   "source": [
    "print(scores)\n",
    "print(scores.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# def plot_confusion_matrix(cm, title='Confusion Matrix'):\n",
    "#     plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "#     plt.title(title)\n",
    "#     plt.colorbar()\n",
    "#     xlocations = np.array(range(len(labels)))\n",
    "#     plt.xticks(xlocations, labels, rotation=90)\n",
    "#     plt.yticks(xlocations, labels)\n",
    "#     plt.ylabel('True label')\n",
    "#     plt.xlabel('Predicted label')\n",
    "\n",
    "# from sklearn.metrics._classification import confusion_matrix\n",
    "# # from card._common_func import plot_confusion_matrix\n",
    "\n",
    "# # data['normAmount'] = StandardScaler().fit_transform(data['Amount'].values.reshape(-1, 1))\n",
    "# cnf_matrix = confusion_matrix(y_test,y_pre)\n",
    "# # class_names = [0,1]\n",
    "# labels = [0,1]\n",
    "# cmap = plt.get_cmap(\"tab20c\")\n",
    "# # outer_colors = cmap(np.arange(4)*4)\n",
    "# # cmap = cmap(np.arange(1,16))\n",
    "\n",
    "# plot_confusion_matrix(cnf_matrix, title='Normalized confusion matrix')\n",
    "# # show confusion matrix\n",
    "# path_file=r'C:\\Users\\文远\\Desktop\\git_test\\数据处理类\\Kaggle-Data-Credit-Card-Fraud-Detection'\n",
    "# plt.savefig(path_file+'\\confusion_matrix.png', format='png')\n",
    "# plt.show()\n",
    "\n",
    "# from sklearn.metrics._classification import confusion_matrix\n",
    "# # from card._common_func import plot_confusion_matrix\n",
    "# from sklearn.metrics import plot_confusion_matrix\n",
    "# cnf_matrix = confusion_matrix(y_test,y_pre)\n",
    "# # 打印混淆矩阵\n",
    "# class_names = [0,1]\n",
    "\n",
    "# plt.figure()\n",
    "# plot_confusion_matrix(cnf_matrix\n",
    "#                       , classes=class_names\n",
    "#                       , title='Confusion matrix')\n",
    "# plt.show()"
   ]
  }
 ]
}